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Phenotypic plasticity is the expression of alternative phenotypes in response to varying environments. Developmental and evolutionary ecology studies addressing phenotypic plasticity often address a common set of questions, including whether there is natural genetic variation for plasticity, the raw material for the evolution of plasticity, and whether phenotypic plasticity is indeed an adaptation to divergent selection regimes. This typically involves scoring both plastic traits and environment-specific fitness in arrays of genotypes replicated within and across multiple environments. After estimating selection differentials or gradients, it can be determined whether plasticity results in phenotypes that better match selection regimes (Pigliucci, 2001; Schmitt et al., 2003). These data can also be used to address a third relevant question: Is plasticity costly? (i.e., all else being equal, are more plastic genotypes less fit than less plastic genotypes?) (Van Tienderen, 1991; DeWitt, 1998; Relyea, 2002)? It is well worth asking this latter question given a discrepancy between the theoretical importance of plasticity costs and evidence about their frequency and magnitude (Scheiner & Berrigan, 1998), and given the straightforward methods available to test this (DeWitt et al., 1998).
Studies documenting significant plasticity costs are much less common than those finding costs that are small in magnitude or negligible, suggesting that past selection has minimized costs (DeWitt, 1998; Sultan & Spencer, 2002). Yet studies typically sample natural populations and therefore lack information about genetic machinery underlying plasticity, including so-called ‘plasticity genes’ or loci that directly respond to environmental stimuli, triggering phenotypic plasticity in one or several traits (Pigliucci, 1996). An exceptional study in this literature documented significant plasticity costs by comparing the viability of transgenic strains of Drosophila melanogaster possessing either enhanced or disabled expression of heat shock proteins (Feder, 1999). Based on that study, it has been suggested that plasticity costs may be detectable only when large, single gene effects are the underlying genetic basis for plasticity (Berrigan & Scheiner, 2004). Yet it is certainly possible that genes of large effect were the basis for plasticity in studies involving natural genotypes, including studies that failed to detect costs. Indeed, Agrawal (2001) has discussed the difficulty of interpreting very small or negligible plasticity costs in the absence of information about plasticity machinery. Clearly, more studies conducted with well-characterized genotypes are needed.
Studies involving transgenic organisms may lack biological realism or be subject to numerous challenges (e.g. confounding effects of allelic variation with genetic background or position effects: Tatar, 2000). Here, we report a new approach for quantifying and interpreting plasticity and its associated costs. The research described in this paper examines two highly plastic flowering time traits in a recombinant inbred population of Arabidopsis thaliana, exposing genotypes to manipulated temperatures mimicking periods of overwintering. This allows assessing each genotype's response to vernalization. It uses a mapping population of recombinant inbred lines (RILs) harboring appropriate variation for plasticity. In this genetically well-informed framework, negative results (i.e. failure to detect costs) may be more interpretable compared with studies sampling directly from natural populations. A comparable approach would be feasible for any species for which RI populations can be created.
Our specific study system offers several additional advantages. First, conducting genotypic selection analyses and plasticity cost analyses with Arabidopsis is generally straightforward. Mean trait values and mean lifetime fitness are readily quantifiable in replicated genotypes. Several studies have successfully used natural genotypes or RILs to conduct genotypic selection analyses, including studies focusing on flowering time plasticity induced by shading (e.g. Dorn et al., 2000; Callahan & Pigliucci, 2002), responses to herbivores (e.g. Mauricio & Rausher, 1997; Weinig et al., 2003), and other factors (reviewed by Pigliucci, 2003). To date, these techniques have not been used to rigorously examine the adaptive significance of vernalization requirements, or to probe the phenological or climatic factors favoring its maintenance or loss. This is somewhat surprising since the underlying mechanism of plasticity to vernalizing temperatures has been intensively studied (Henderson et al., 2003) and among-population variation for vernalization requirement has been extensively documented (Karlsson et al., 1993; Nordborg & Bergelson, 1999; Stinchcombe et al., 2004).
Accordingly, a second advantage of using RI populations of A. thaliana is that vernalization responses are known to exhibit variable plasticity among ecotypes. Responses are typically dichotomized as either present (winter annuals) or absent (summer annuals). In winter annuals, flowering is chronologically and developmentally delayed in the absence of vernalization. By contrast, in summer annuals, flowering occurs early regardless of vernalization treatment (Nordborg & Bergelson, 1999). Most of the variation in vernalization-mediated flowering habit is controlled by the FRIGIDA (FRI) locus and its interaction with FLOWERING LOCUS C (FLC). Elucidation of FRI and FLC function has led to extensive characterization of FRI alleles in many wild ecotypes (Johanson et al., 2000). Functional and nonfunctional alleles of the FRI gene segregate in an established population of recombinant inbred lines (RILs) (Wilson et al., 2001); this population harbors extensive, bi-modal variation for flowering habit. The large number and diversity of RILs within a mapping population can increase statistical power for testing the significance of selection gradients, a particularly crucial issue in searches for plasticity costs (DeWitt, 1998; van Kleunen et al., 2000; Pigliucci, 2001).
A third, general advantage of our approach is that RI populations are well-suited to quantitative trait loci (QTL) analysis, including efforts to associate plastic phenotypes and fitness with QTL–environment interactions (Weinig & Schmitt, 2004). Here, in multiple RI lines we score the environment-specific means for a trait, the mean plasticity associated with that trait, and mean fitness. We then examine QTL, whether effects of QTL are environmentally variable, and whether trait or plasticity QTLs colocate in the same chromosomal regions as well-characterized plasticity genes (e.g. FRI or other well-studied environmental signal transduction genes). This makes it possible to look for overlap between chromosomal regions harboring fitness QTL and the QTL affecting plastic traits. The presence of such overlap is a first step in determining whether genes regulating plastic traits exert pleiotropic effects on fitness.
Our study analyzed a single experiment by integrating four quantitative genetic analyses. First, it quantified two flowering traits and their associated plasticity using the Col-gl1 × Kas recombinant inbred lines, originating from parents lacking (Col-gl1) and possessing (Kas) a vernalization requirment (Wilson et al., 2001). Because this population harbors a polymorphism at the FRI locus, RILs possessing a functional FRI allele should respond to brief, partial vernalization by showing delayed flowering, while RILs with a null allele should exhibit constitutive early flowering. A lengthy, full vernalization treatment should minimize this disparity, with all RILs flowering early. Second, after confirming these predictions, we conducted a genotypic selection analysis using environment-specific relative fitness data to estimate and test the significance of selection differentials and gradients on these two flowering traits. Third, by combining this information with data about the plasticity of each RIL, we quantified plasticity costs with multiple regression analyses within each environment. Fourth, using QTL mapping approaches, we examined the genetic architecture of plastic flowering-time traits, confirmed the importance of the FRI locus, and examined potential pleiotropic effects of trait QTL on fitness.
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Studies documenting among-population variation for a vernalization requirement (e.g. Karlsson et al., 1993; Lee et al., 1993; Nordborg & Bergelson, 1999) have provided valuable information leading to current understanding of FRI-FLC function and its molecular population genetics (Johanson et al., 2000; Le Corre et al., 2002; Michaels et al., 2003; Caicedo et al., 2004; Stinchcombe et al., 2004). Our study corroborates and further integrates these previous efforts in several ways. First, we found the expected variation among RILs for flowering habit. Second, genotypic selection analyses indicated that earlier flowering may be favored, especially when mild overwintering conditions fail to fully vernalize vegetative rosettes. Third, plasticity cost analyses suggest more plastic genotypes may have lower fitness after experiencing mild overwintering conditions, over and above the reduction in fitness that results from delayed flowering. Fourth, mapping techniques detected QTL with environment-specific effects on flowering, including a major QTL that colocated in the immediate vicinity of FRI but that did not overlap with the significant and nearly significant fitness QTL detected. This study has increased our awareness of both advantages and pitfalls associated with developmental and evolutionary ecology studies involving RI populations. As we discuss interpretation of our results in greater detail, we also offer suggestions for refining future analyses and for designing either lab or field experiments.
Adaptive significance of a vernalization requirement
Previous studies have quantified selection on flowering time in Arabidopsis thaliana, and results have varied. In many different glasshouse environments, Clauss & Aarssen (1994) documented positive correlations between vegetative mass and fecundity. A glasshouse study by Dorn et al. (2000) documented selection in high and low density treatments and in the presence and absence of simulated shade. Chronologically later flowering was selected in all environments, often in combination with selection favoring flowering at a later developmental stage (more leaves), at larger size (larger rosette leaf length), or both. Contrasting examples include field and potted-plant studies conducted in shaded and unshaded habitats that found similar patterns of selection, with chronologically earlier bolting consistently favored, sometimes in combination with selection favoring developmentally later bolting (Callahan & Pigliucci, 2002). In a growth chamber study of time to bolting, leaf number at bolting, and size at reproduction in the Col × LerRI population of Arabidopsis, selection gradients favor bolting earlier chronologically but developmentally later and at a larger size (Mitchell-Olds, 1996). The results of these latter two studies are consistent with the one reported here, as well as with life-history theory that predicts the inability of selection to simultaneously increase size and reduce age at first reproduction (Stearns, 1992).
We expected to find genotypic variation, since any given RI line possessed either a functional FRI allele or a fri mutant. In the partial vernalization treatment, among-RIL variation in both flowering time and fitness were magnified, and RILs that inherited a functional FRI allele from the Kashmir parent tended to exhibit delayed flowering. If such a treatment corresponds well with milder or briefer winters in the field, then a fitness disadvantage may be experienced by plastic genotypes (i.e. possessing a functional FRI allele results in plastically and maladaptively delaying flowering). Interestingly, a weak tendency for functional FRI to be less common at low latitudes has been documented, as well as ample evidence for convergent evolution to lose a requirement via mutations in either FRI or FLC (Johanson et al., 2000; Le Corre et al., 2002; Michaels et al., 2003), although at any given latitude there are examples of populations possessing a vernalization requirement.
Whether selection regimes, flowering behavior and fitness estimates associated with full and partial vernalization treatments in the lab reasonably reflect field conditions remains an important and open question. We note that we may have lowered variance for flowering time and fitness by growing plants in individual pots arranged at uniform densities, protected from herbivores and pathogens, amply watered, and not exposed to viability selection (e.g. Pigliucci & Marlow, 2001). Low variance would likely bias against finding significant gradients, plasticity costs, or fitness QTLs. We recently initiated an overwintering field experiment examining vernalization-mediated plasticity in a larger set of Col-gl1 × Kas RILs. It will allow us to examine whether significant genotypic variance components can be detected under field conditions, where environmental variance can be large. It will also investigate whether the mean relative fitness estimates for each RIL in lab studies correspond with mean relative fitness estimated in the field.
Is a vernalization requirement costly?
Previous studies have attempted to detect plasticity costs by either sampling many highly diverse genotypes (Dorn et al., 2000; van Kleunen et al., 2000; Steinger et al., 2003) or by engineering genotypes with well-characterized plasticity machinery (Krebs & Feder, 1997; Feder, 1999). Our study combined these approaches via analysis of genetically well-characterized RI lines. The plasticity cost detected was environment-specific (sensu Sultan & Spencer, 2002), detected only after partial vernalization. Also, the cost detected was not significant after applying a more stringent rejection criteria (Scheiner & Berrigan, 1998; Relyea, 2002). Negative results are frequent in studies attempting to detect plasticity costs, and can be difficult to interpret (Agrawal, 2001). Here, the plasticity costs detected were neither highly significant nor global (sensu Sultan & Spencer, 2002) yet we know that a gene of large effect (e.g. FRI) is the basis for plasticity. Such a result is consistent with past selection minimizing such costs in the population from which parental genotypes were sampled (Dewitt, 1998; Sultan & Spencer, 2002).
If a less stringent rejection criterion is applied, we conclude that the detected costs are significant. Indeed, the cost detected is rather large in magnitude (a gradient of −0.49). Moreover, its environmental specificity (in the partial vernalization treatment only) is consistent with current understanding of the FRI-FLC pathway. Specifically, up-regulation of FLC by FRI could lead to two types of nonmutually exclusive costs (DeWitt et al., 1998): genetic costs (i.e. due to pleiotropic effects of one or both genes) or maintenance costs (i.e. due to energy required for transcription and expression of FLC and downstream machinery, which must be maintained in growing plant tissues after vernalization: Michaels & Amasino, 2000, 2001). Both types are likely to differ between plastic and nonplastic genotypes. Both should also be lower after exposure to cold, which blocks up-regulation of FLC by FRI (Michaels & Amasino, 2000).
If FRI-mediated plasticity results in maladaptively late-flowering phenotypes after milder or briefer winters and if, additionally, early flowering is selected in such environments, then why do FRI alleles persist, particularly at low latitudes? Le Corre et al. (2002) speculate that purifying selection may have acted during the last glacial periods, but since then diversifying selection has targeted the FRI-FLC mechanism. Also, functional FRI alleles may increase tolerance to drought (Stinchcombe et al., 2004), and several lines of evidence indicate pleiotropic effects of FRI and FLC on water use efficiency (McKay et al., 2003). Thus, selection against FRI-mediated plasticity in milder temperature regimes may be counterbalanced by selection to enhance drought tolerance.
A novel benefit of using RILs for selection gradient analyses and plasticity cost analyses is that QTL mapping techniques can be employed to examine whether QTLs for the traits under study map to the vicinity of well-characterized plasticity genes, whether those QTLs exhibit environment specific expression, and whether they colocated with QTLs for fitness.
We were successful in detecting one QTL that affected both flowering traits and mapped to the immediate vicinity of FRI. Among-RIL variance for fitness apparently mapped to unique QTL rather than overlapping with QTL for plastic traits. This suggests that selection favoring earlier bolting and against plastic RILs was not due to pleiotropy (or tight linkage) of genes affecting both bolting behavior and fruit production. Possibly, days to bolting was correlated with an unscored trait, and a QTL for that trait overlaps with the fitness QTL detected. Alternatively, the small number of lines used in this study could have resulted in insufficient statistical power to detect all pertinent trait and fitness QTLs.
These are examples of how QTL analyses can go beyond verifying that among-RIL variance maps to QTL harboring candidate genes, asking important questions about the adaptive significance of plasticity, plasticity costs, and pleiotropic impacts on fitness exerted by QTL (and candidate plasticity genes) involved in regulating plastic phenotypes. The answers provided by this study are provisional, and detecting multiple QTL, including more fitness QTL and trait QTL with minor effects, is a critical goal for future studies. This will necessarily involve phenotyping a larger number of RILs, possibly from a larger RI population harboring segregating variation at both FRI and FLC (Bay-0 × Sha: Loudet et al., 2002). Ongoing field studies will permit us to examine not only whether there is overlap between trait and fitness QTL in the field, but also whether the same QTL are acting in lab and field conditions (Weinig & Schmitt, 2004). In lab or field studies, working with a larger set of RILs will be desirable to improve not only QTL detection, but also estimation and testing of selection gradients, including gradients used to quantify plasticity costs.